linear models with L1 + L2 regularization. As global optimization objective is
strongly-convex, the optimizer optimizes the dual objective at each step. The
optimizer applies each update one example at a time. Examples are sampled
uniformly, and the optimizer is learning rate free and enjoys linear convergence
rate.

Args:

sparse_example_indices: A list of Tensor objects with type int64.
a list of vectors which contain example indices.

sparse_feature_indices: A list with the same length as sparse_example_indices of Tensor objects with type int64.
a list of vectors which contain feature indices.

sparse_feature_values: A list of Tensor objects with type float32.
a list of vectors which contains feature value
associated with each feature group.

dense_features: A list of Tensor objects with type float32.
a list of matrices which contains the dense feature values.

example_weights: A Tensor of type float32.
a vector which contains the weight associated with each
example.

example_labels: A Tensor of type float32.
a vector which contains the label/target associated with each
example.

sparse_indices: A list with the same length as sparse_example_indices of Tensor objects with type int64.
a list of vectors where each value is the indices which has
corresponding weights in sparse_weights. This field maybe omitted for the
dense approach.

sparse_weights: A list with the same length as sparse_example_indices of Tensor objects with type float32.
a list of vectors where each value is the weight associated with
a sparse feature group.

dense_weights: A list with the same length as dense_features of Tensor objects with type float32.
a list of vectors where the values are the weights associated
with a dense feature group.

example_state_data: A Tensor of type float32.
a list of vectors containing the example state data.